Improving Odometric Accuracy for an Autonomous Electric Cart

Abstract

In this paper, a study of the odometric system for the autonomous cart Verdino, which is an electric vehicle based on a golf cart, is presented. A mathematical model of the odometric system is derived from cart movement equations, and is used to compute the vehicle position and orientation. The inputs of the system are the odometry encoders, and the model uses the wheels diameter and distance between wheels as parameters. With this model, a least square minimization is made in order to get the nominal best parameters. This model is updated, including a real time wheel diameter measurement improving the accuracy of the results. A neural network model is used in order to learn the odometric model from data. Tests are made using this neural network in several configurations and the results are compared to the mathematical model, showing that the neural network can outperform the first proposed model.

A Real Time Adaptive Odometric System based on Variable Covariance Sensor Fusion

“…In Borenstein (1998) and Maddahi (2005) a similar method is applied for mobile robots where the robot is programmed to follow an approximate square-path to correct the parameters. Toledo et al (2018) presents an autonomous cart with an improvement in odometric accuracy using a optical sensor to obtain the wheel diameter in real time and a neural network trained with GPS data to learn odometry model improving the pose. However, the main disadvantages of the previous models are the requirement of external and precise sensors or they are offline, therefore they can't adjust online the parameters.…”

Improving Robot Localization Using Doppler-Based Variable Sensor Covariance Calculation

“…The sensor's output is not affected by the environment or people, it only depends on the reflection from the floor. Equation (6) shows the device frequency output, where F d is the output Doppler frequency after the mixer and the subtraction, v r is the speed of the robot, f 0 is the transmission frequency of 10.525 Ghz, c is the speed of light and θ is the angle between the direction of motion and the axis of the module.…”

“…The system captures the movement of the wheels in real time. It is one of the main sensors for a robotic prototype and the basis for any localization system [6]. The position and orientation is obtained by integrating wheel displacement.…”

Covarianza dinamica en un sistema odometrico real para la mejora de la localizacion con sensores a bordo

“…El sensor más importante con el que cuenta un robot móvil para su localización es el sistema odométrico. Este normalmente consiste en un encoder óptico incremental acoplado a cada uno de los ejes motores del robot [15]. Es un sensor preciso, económico y sencillo de interpretar y procesar, sin embargo su principal limitación es que la posición se calcula incrementalmente, con lo que los pequeños errores que se van generando en la estimación del movimiento de las ruedas, se van acumulando y terminan generando un error muy grande.…”